Identification of Genomic Signatures for Colorectal Cancer Survival Using Exploratory Data Mining.

Clinicopathological presentations are critical for establishing a postoperative treatment regimen in Colorectal Cancer (CRC), although the prognostic value is low in Stage 2 CRC. We implemented a novel exploratory algorithm based on artificial intelligence (explainable artificial intelligence, XAI) that integrates mutational and clinical features to identify genomic signatures by repurposing the FoundationOne Companion Diagnostic (F1CDx) assay. The training data set (n = 378) consisted of subjects with recurrent and non-recurrent Stage 2 or 3 CRC retrieved from TCGA. Genomic signatures were built for identifying subgroups in Stage 2 and 3 CRC patients according to recurrence using genomic parameters and further associations with the clinical presentation. The summarization of the top-performing genomic signatures resulted in a 32-gene genomic signature that could predict tumor recurrence in CRC Stage 2 patients with high precision. The genomic signature was further validated using an independent dataset (n = 149), resulting in high-precision prognosis (AUC: 0.952; PPV = 0.974; NPV = 0.923). We anticipate that our genomic signatures and NCCN guidelines will improve recurrence predictions in CRC molecular stratification.

Integrating transformer-based machine learning with SERS technology for the analysis of hazardous pesticides in spinach.

This study introduces an innovative strategy for the rapid and accurate identification of pesticide residues in agricultural products by combining surface-enhanced Raman spectroscopy (SERS) with a state-of-the-art transformer model, termed SERSFormer. Gold-silver core-shell nanoparticles were synthesized and served as high-performance SERS substrates, which possess well-defined structures, uniform dispersion, and a core-shell composition with an average diameter of 21.44 ± 4.02 nm, as characterized by TEM-EDS. SERSFormer employs sophisticated, task-specific data processing techniques and CNN embedders, powered by an architecture features weight-shared multi-head self-attention transformer encoder layers. The SERSFormer model demonstrated exceptional proficiency in qualitative analysis, successfully classifying six categories, including five pesticides (coumaphos, oxamyl, carbophenothion, thiabendazole, and phosmet) and a control group of spinach data, with 98.4% accuracy. For quantitative analysis, the model accurately predicted pesticide concentrations with a mean absolute error of 0.966, a mean squared error of 1.826, and an R2 score of 0.849. This novel approach, which combines SERS with machine learning and is supported by robust transformer models, showcases the potential for real-time pesticide detection to improve food safety in the agricultural and food industries.

Association of Baseline Pre-Diagnosis and Post-Diagnosis Obesity and Weight Change with Cardiovascular Risk and Survival Among Nonmetastatic Prostate Cancer Survivors.

INTRODUCTION: Obesity in prostate cancer survivors may increase mortality. Better characterization of this effect may allow better counseling on obesity as a targetable lifestyle factor to reduce mortality in prostate cancer survivors. The purpose of this study was to determine whether pre- and post-diagnostic obesity and weight change affect all-cause mortality, cardiovascular disease specific mortality, and prostate cancer specific mortality in patients with nonmetastatic prostate cancer. PATIENTS AND METHODS: We performed a retrospective cohort analysis of 5,077 patients diagnosed with localized prostate cancer from 1997 to 2017 with median follow-up of 15.5 years. The Utah Population Database linked to the Utah Cancer Registry was used to identify patients at a variety of treatment centers. RESULTS: Pre-diagnosis obesity was associated with a 62% increased risk of cardiovascular disease specific mortality and a 34% increased risk of all-cause mortality (HR 1.62, 95% CI 1.05-2.50; HR 1.34, 95% CI 1.07-1.67, respectively). Post-diagnosis obesity increased the risk of cardiovascular disease specific mortality (HR 1.83, 95% CI 1.31-2.56) and all-cause mortality (HR 1.37, 95% CI 1.16-1.64) relative to non-obese men. We found no association between pre-diagnostic obesity or post-diagnostic weight gain and prostate cancer specific mortality. CONCLUSION: Our study strengthens the conclusion that pre-, post-diagnostic obesity and weight gain increase cardiovascular disease and all-cause mortality but not prostate cancer specific mortality compared to healthy weight men. An increased emphasis on weight management may improve mortality for prostate cancer survivors who are obese.